COMPUTATIONAL CANCER DETECTION OF PATHOLOGICAL IMAGES BASED ON AN OPTIMIZATION METHOD FOR COLOR-INDEX LOCAL AUTO-CORRELATION FEATURE EXTRACTION

被引：0

作者：

Qu, Jia ^{[1
]}

Nosato, Hirokazu ^{[2
]}

Sakanashi, Hidenori ^{[2
]}

Takahashi, Eiichi ^{[2
]}

Terai, Kensuke ^{[3
]}

Hiruta, Nobuyuki ^{[3
]}

机构：

[1] Univ Tsukuba, Dept Intelligent Interact Technol, Tsukuba, Ibaraki 305, Japan

[2] Natl Inst Adv Ind Sci & Technol, Tokyo, Japan

[3] Toho Univ, Sakura Med Ctr, Dept Surg Pathol, Tokyo, Japan

来源：

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) | 2014年

关键词：

cancer detection; pathological images; CILAC; feature extraction; optimization;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Aiming to lessen the burdens of the pathologist with efficient diagnosis assistance, this paper proposes a cancer detection method for pathological images utilizing color features based on color-index local auto-correlations (CILAC), applied to color-indexed images to utilize co-occurrence information about indexed pixels. Moreover, a method for the automatic optimization of feature extraction is also proposed. Based on a database including both benign and cancerous pathological images, experimental results show enhanced performance compared to prior research, which demonstrate the effectiveness of the proposed cancer detection method.

引用

页码：822 / 825

页数：4

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